Zhiguang Cao
Artificial Intelligence · IEEE/CAA Journal of Automatica Sinica · 2019–2023
A researcher in the Science corpus: 5 papers; trusts 22 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 53 citations into it
- Industrial and Manufacturing Engineering — 30 citations into it; #2,135 of 2,521 by trust here, #1,884 by citations
- Computer Vision and Pattern Recognition — 21 citations into it
- Model Architectures — 16 citations into it
- Reinforcement Learning — 13 citations into it
- Optimization & Training — 7 citations into it
- Natural Language Processing — 6 citations into it
Whom Zhiguang Cao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Ronald J. Williams
- Diederik P. Kingma
- Geoffrey E. Hinton
- Kaiming He
- Imed Kacem
- Oriol Vinyals
- Sergey Ioffe
- Alex Krizhevsky
- Guohui Zhang
Who trusts Zhiguang Cao overall
Papers from Zhiguang Cao's perspective
Ordered by how strongly the researchers Zhiguang Cao trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM (2019) — 444 citations
- Flexible Job-Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning (2023) — 373 citations
- Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning (2020) — 463 citations
- Learning Improvement Heuristics for Solving Routing Problems (2019) — 420 citations
- A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems (2019) — 458 citations